Automated fault location in a power system with distributed generations using radial basis function neural networks

H. Zayandehroodi, Azah Mohamed, H. Shareef, M. Mohammadjafari

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

High penetration of Distributed Generation (DG) units will have unfavorable impacts on the traditional fault location methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This study presents an automated fault location method using Radial Basis Function Neural Network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is determined first by normalizing the fault currents of the main source. Then to determine the fault location, two RBFNNs have been developed for various fault types. The first RBFNN is used for detraining fault distance from each source and the second RBFNN is used for identifying the exact faulty line. Several case studies have been used to verify the accuracy of the method. Furthermore, the results of RBFNN and the conventional Multi Layer Perception Neural Network (MLPNN) are also compared. The results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.

Original languageEnglish
Pages (from-to)3032-3041
Number of pages10
JournalJournal of Applied Sciences
Volume10
Issue number23
Publication statusPublished - 2010

Fingerprint

Electric fault location
Distributed power generation
Neural networks
Electric fault currents

Keywords

  • Distributed generation (DG)
  • Fault location
  • Perception neural network (MLPNN)
  • Power system
  • Radial basis function neural network (RBFNN)

ASJC Scopus subject areas

  • General

Cite this

Zayandehroodi, H., Mohamed, A., Shareef, H., & Mohammadjafari, M. (2010). Automated fault location in a power system with distributed generations using radial basis function neural networks. Journal of Applied Sciences, 10(23), 3032-3041.

Automated fault location in a power system with distributed generations using radial basis function neural networks. / Zayandehroodi, H.; Mohamed, Azah; Shareef, H.; Mohammadjafari, M.

In: Journal of Applied Sciences, Vol. 10, No. 23, 2010, p. 3032-3041.

Research output: Contribution to journalArticle

Zayandehroodi, H, Mohamed, A, Shareef, H & Mohammadjafari, M 2010, 'Automated fault location in a power system with distributed generations using radial basis function neural networks', Journal of Applied Sciences, vol. 10, no. 23, pp. 3032-3041.
Zayandehroodi, H. ; Mohamed, Azah ; Shareef, H. ; Mohammadjafari, M. / Automated fault location in a power system with distributed generations using radial basis function neural networks. In: Journal of Applied Sciences. 2010 ; Vol. 10, No. 23. pp. 3032-3041.
@article{d4832605a13d40f6956cca9c508ac13c,
title = "Automated fault location in a power system with distributed generations using radial basis function neural networks",
abstract = "High penetration of Distributed Generation (DG) units will have unfavorable impacts on the traditional fault location methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This study presents an automated fault location method using Radial Basis Function Neural Network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is determined first by normalizing the fault currents of the main source. Then to determine the fault location, two RBFNNs have been developed for various fault types. The first RBFNN is used for detraining fault distance from each source and the second RBFNN is used for identifying the exact faulty line. Several case studies have been used to verify the accuracy of the method. Furthermore, the results of RBFNN and the conventional Multi Layer Perception Neural Network (MLPNN) are also compared. The results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.",
keywords = "Distributed generation (DG), Fault location, Perception neural network (MLPNN), Power system, Radial basis function neural network (RBFNN)",
author = "H. Zayandehroodi and Azah Mohamed and H. Shareef and M. Mohammadjafari",
year = "2010",
language = "English",
volume = "10",
pages = "3032--3041",
journal = "Journal of Applied Sciences",
issn = "1812-5654",
publisher = "Asian Network for Scientific Information",
number = "23",

}

TY - JOUR

T1 - Automated fault location in a power system with distributed generations using radial basis function neural networks

AU - Zayandehroodi, H.

AU - Mohamed, Azah

AU - Shareef, H.

AU - Mohammadjafari, M.

PY - 2010

Y1 - 2010

N2 - High penetration of Distributed Generation (DG) units will have unfavorable impacts on the traditional fault location methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This study presents an automated fault location method using Radial Basis Function Neural Network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is determined first by normalizing the fault currents of the main source. Then to determine the fault location, two RBFNNs have been developed for various fault types. The first RBFNN is used for detraining fault distance from each source and the second RBFNN is used for identifying the exact faulty line. Several case studies have been used to verify the accuracy of the method. Furthermore, the results of RBFNN and the conventional Multi Layer Perception Neural Network (MLPNN) are also compared. The results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.

AB - High penetration of Distributed Generation (DG) units will have unfavorable impacts on the traditional fault location methods because the distribution system is no longer radial in nature and is not supplied by a single main power source. This study presents an automated fault location method using Radial Basis Function Neural Network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is determined first by normalizing the fault currents of the main source. Then to determine the fault location, two RBFNNs have been developed for various fault types. The first RBFNN is used for detraining fault distance from each source and the second RBFNN is used for identifying the exact faulty line. Several case studies have been used to verify the accuracy of the method. Furthermore, the results of RBFNN and the conventional Multi Layer Perception Neural Network (MLPNN) are also compared. The results showed that the proposed method can accurately determine the location of faults in a distribution system with several DG units.

KW - Distributed generation (DG)

KW - Fault location

KW - Perception neural network (MLPNN)

KW - Power system

KW - Radial basis function neural network (RBFNN)

UR - http://www.scopus.com/inward/record.url?scp=78049400962&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78049400962&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:78049400962

VL - 10

SP - 3032

EP - 3041

JO - Journal of Applied Sciences

JF - Journal of Applied Sciences

SN - 1812-5654

IS - 23

ER -